Reservoir computing determined by nonlinear weight dynamics in Gd-doped CeO2/CeO2 bi-layered oxide memristors

Abstract

Reservoir computing (RC) is an effective framework for processing spatiotemporal signals. Memristors are well-suited for physical reservoirs in hardware-based RC systems due to their nonlinear functions and memory characteristics. This study experimentally demonstrates an RC system using Pt/Gd-doped CeO2(GDC)/CeO2/Pt memristors. These devices exhibit time-dependent weight updates and decay characteristics, which are critical for extracting spatial and temporal features in RC applications. While previous research has explored implementing RC systems by exploiting the nonlinearity of memristors, there is a lack of systematic research on factors affecting the nonlinearity of memristors and analyzing the reservoir states. Using the time-dependent dynamics of Pt/GDC/CeO2/Pt memristors, this study extracts reservoir states under different pulse conditions and systematically analyzes the factors affecting the extraction of these states. Our findings demonstrate that nonlinearly mapped reservoir states can be linearly separated to achieve high-performance recognition and prediction in complex spatiotemporal tasks in RC systems. Finally, the RC performance of the memristor shows up to 90.5% accuracy in 4-bit pattern verification using the Modified National Institute of Standards and Technology (MNIST) database.